1. Field of the Invention
The invention relates to a diagnostic device and method for monitoring the operation of a control loop having a controlled system comprising a valve as an actuator.
2. Description of the Related Art
The maintenance and servicing of automated plants can be improved by monitoring the correct functioning of plant segments or components. In the case of insufficient reliability, measures for maintenance, servicing and fault correction can be targeted at the correct place in the plant. In numerous control loops in industrial plants, valves are used as actuators in controlled systems. The most common cause of problems and faults in control loops of this kind is increased friction in the valve, which can be caused by ageing of the seal, coatings on the seal or valve stem or due to packing gland seals that have been excessively tightened. Increased valve friction can impair the accuracy and control quality of the control loop. To enable measures for servicing or control optimization to be implemented in a timely and targeted selective manner at the correct place in the plant, in the case of the insufficient performance of individual control loops, it would be advantageous for the control quality of control loops to be monitored permanently and automatically.
EP 1 528 447 B1 discloses a diagnostic method for monitoring the operation of a control loop. In the case of a substantially stationary state, i.e., with an extensively constant setpoint selection, the variance of a sequence of actual data is determined as a stochastic characteristic and evaluated for an analysis of the state of the control loop. In the case of the excitation of the control loop corresponding to a setpoint jump, the relative overshoot or the settling ratio, i.e., the quotient of the rise time and settling time of the controlled variable are evaluated as deterministic features for an analysis of the control loop status.
The book “Detection and Diagnosis of Stiction in Control Loops” by Jelali, M. And Huang, B., Springer-Verlag, London, 2010 describes the assessment of the friction of valves used as actuators in control loops for control loop monitor. To elucidate the frictional effects observed thereby, FIG. 2 is a diagram showing an idealized characteristic curve. A manipulated variable u, which is output by a controller arranged upstream of the valve, is plotted on the abscissa and the actual value x of the actual valve setting is plotted on the ordinate. In the case of a valve that is free of frictional effects, according to a characteristic curve 20, the actual value x would correspond exactly to the manipulated variable u. Therefore, the characteristic curve 20 is shown as a straight line. If, on the other hand, a valve has experienced static friction and/or sliding friction, the actual valve setting x will differ from the manipulated variable u output by the controller. This behavior is elucidated with reference to a schematic characteristic curve 21. A parameter J designates the height of a jump after the valve has broken out of the static friction. A parameter S designates the slip and corresponds to the sum of the width of a dead band DB and the jump height of the parameter J. Two parameters fd and fs are an alternative form of describing the frictional behavior and can be used as alternatives to the above-explained parameters S and J. In this context, the parameter fs designates the normalized sum of all the frictional effects and the parameter fd the normalized residual Coulomb friction, where the residual friction relates to slip friction and may have a smooth transition to static friction. The width of the dead band DB results from the degree of the slip friction. The parameters S, J, fd and fs are normalized parameters, i.e., they relate to the size of the control range and can, for example, be expressed as a percentage of the control range. The parameter S indicates the necessary change to the control signal u that has to be applied to ensure that the valve moves on a reversal of the direction of movement. The relationship between the parameter sets is as follows:S=fs+fd andJ=fs−fd. 
The characteristic curve 21 of the frictional behavior describes a parallelogram. The segments extending parallel to the abscissa, in which, despite the varying manipulated variable u, the valve setting x remains unchanged, result from the static friction. As soon as the static friction has been overcome, the valve breaks free and executes a jump corresponding to the segments extending in parallel to the ordinate. Unlike the characteristic curve 20 without frictional effects, following the execution of a jump, a constant lag error is present. This results from the slip friction in the valve. If one of the two effects is not present, the characteristic curve 21 describing the frictional behavior of the valve is altered correspondingly. Similarly, an intensification of the frictional effects results in a corresponding change in the profile of the characteristic curve 21. The estimation of the aforementioned friction parameters is also helpful when there is no position feedback, i.e., the valve setting x cannot be measured directly.
Literature, such as the aforementioned book by Jelali and Huang, contains numerous methods for the identification of static friction. This literature frequently refers to static friction as “stiction” a portmanteau of the words “static” and “friction”. Methods for the identification of stiction provide binary information on its presence, but are not always reliable. The method that is most suitable for identifying stiction is dependent upon the boundary conditions. However, there are no specific suggestions as to which method should be given preference for control loop monitoring.
The aforementioned book by Jelali and Huang describes the following methods for the identification of stiction:
Method a: uses typical profiles, such as valve setting jams, while manipulated variable u rises or falls.
Method b: uses the fact that a parallelogram forms in the scatter plot, a diagram showing profiles of the value pairs of manipulated variable u and actual value x of the valve setting acquired during the operation of a control loop.
Method c: determines the shape of the scatter plot of the position feedback from the manipulated variable u, and hence is an attempt to find typical profiles.
A cross correlation function (CCF) is a method that determines whether the CCF (cross correlation function) between the manipulated variable u and the actual value x of valve setting is even or uneven.
A curve-shape method is a method that compares the profile of the manipulated variable u with a sinusoidal signal and a triangular signal.
In the above list, the names of the individual methods were taken from the aforementioned book by Jelali and Huang and merely translated into German.
In order to estimate the intensity of the impacts of the frictional influences, the aforementioned book describes two methods involving a similar procedure under the heading “Stiction Estimation”, but both of these require very high computing complexity. Both methods use the familiar Hammerstein model to simulate valve behavior mathematically via a model. In the Hammerstein model, the frictionless dynamic behavior of the valve is represented by a linear dynamic submodel. A nonlinear submodel arranged upstream thereof is intended to simulate nonlinear behavior of the valve, which is substantially based on the above-described frictional effects. According to literature, the parameters of the two submodels with which the lowest deviations between the behavior of the virtual model and that of the real valve are achieved should be estimated in a single method.
In this context, two optimization problems, one nonlinear and one linear, which are also coupled to one another, need to be solved simultaneously. For the identification of the Hammerstein model, i.e., in order to find the best possible parameter set, a global search for optimal parameters of the nonlinear part is performed. In this context, the method of least error squares, “least squares” estimation, is used to identify the optimal linear submodel again in each case for each iteration step.
In order to determine the nonlinear submodel, it is first necessary to select a model type suitable for the valve. He's model as depicted in FIG. 3.1 of the aforementioned book by Jelali and Huang is selected for an as realistic as possible representation of the valve behavior. This entails an optimization problem with the two parameters fs and fd. To restrict the search for parameters with the best possible conformance between the Hammerstein model and the real valve, the measured data and physical considerations can be used to define the following limits for the parameters fs and fd:
fs≥0, fd≥0: both parameters must be greater than or at least equal to 0 because negative values do not make any physical sense.
fs+fd<Smax: an upper limit Smax of the parameter Scan be determined from the measured data as the difference between the maximum and minimum value of the manipulated variable u. Neither can the dead band DB be greater than this value.
fd≤fs: for physical reasons, the parameter S is greater or at least equal to the parameter J because the parameter J forms a part of the parameter S.
These limits result in a triangular search space 30, as shown in the diagram in FIG. 3, for the search for optimal parameters fs, plotted on the abscissa, and fd, plotted on the ordinate. Areas i, ii, iii and iv characterize different states of the valve with respect to the prevailing friction:
Area i: there is low friction—the valve is freely mobile,
Area ii: a comparatively large dead band DB and predominantly slip friction,
Area iii: so-called stick-slip behavior with predominantly static friction,
Area iv: a broad dead band DB with simultaneous stick-slip behavior.
The results of the model identification in all areas outside area i can be assessed as an indication of the presence of stiction that can have a negative impact on the control loop behavior.
To identify the model with which the best possible parameters of the Hammerstein model are sought, the aforementioned book by Jelali and Huang suggests a random search or genetic algorithms that unfavorably require a very high number of iterative steps. The practical implementation of the method named therein can also give rise to several problems:
A discrete-time least-squares estimation can entail various numerical problems, such as in connection with measuring noise or the choice of sampling interval, which complicate the estimation of the optimal parameters.
No previous findings are available with respect to the path dynamics and no suitable discrete-time model type is selected.